Introduction to R
and {circumplex}

SITAR 2022 | 2022-07-30
Jeffrey Girard | Univ. of Kansas

Overview

Instructor

Jeffrey Girard, PhD
www.jmgirard.com
jmgirard@ku.edu

Background

  • Assistant Professor, University of Kansas
  • Research Postdoc, Carnegie Mellon University
  • PhD Student, University of Pittsburgh

Research Areas

  • Psychological Assessment
  • Affective/Interpersonal Communication
  • Applied Statistics and Machine Learning
  • Data Science and Software Engineering

Overview

R Rationale

  1. Think of your computer as the engine of a car
    • It provides raw power for computation
  1. The R language is like the controls for the car
    • It lets you apply and direct that power
  1. RStudio is like a fancy dashboard for the car
    • It adds extra information and convenience
  1. An R package is like an add-on for the car
    • It adds new features and capabilities

Overview

Workshop Goals

  • This is a beginner-friendly workshop aimed at social scientists with little to no experience in R
  • My goal is to “grant your learner’s permit”
    • How to navigate around RStudio
    • How to do calculations in R
    • How to import data into R
  • I will introduce you to the {circumplex} package
    • How to score and norm an instrument
    • How to use the Structural Summary Method
    • How to generate tables and figures

Overview

Introduction to R

Installing R

Windows

  1. Open a web browser
  2. Visit cloud.r-project.org
  3. Click “Download R for Windows”
  4. Click the “base” subdirectory link
  5. Click “Download R-4.X.X” (e.g., 4.2.1)
  6. Run the downloaded .exe file
  7. Select all the default options
  8. Complete the installation wizard

Mac OS

  1. Open a web browser
  2. Visit cloud.r-project.org
  3. Click “Download R for macOS”
  4. Click “R-4.X.X.pkg” (e.g., 4.2.1)
  5. Run the downloaded .pkg file
  6. Select all the default options
  7. Complete the installation wizard

Introduction to R

Installing RStudio

Windows

  1. Open a web browser
  2. Visit rstudio.com/download
  3. Scroll down until you find the table
    under the “All Installers” section
  4. Find the row for “Windows 10/11”
  5. Click “RStudio-2022.XX.X-XXX.exe”
  6. Run the downloaded .exe file
  7. Select all the default options
  8. Complete the installation wizard

Mac OS

  1. Open a web browser
  2. Visit rstudio.com/download
  3. Scroll down until you find the table
    under the “All Installers” section
  4. Find the row for “macOS 10.15+”
  5. Click “RStudio-2022.XX.X-XXX.dmg”
  6. Run the downloaded .dmg file
  7. Drag the RStudio icon to your
    Applications folder (if you want)

Introduction to R

RStudio Window

Introduction to R

R will Grant your Wishes

  • R is like a well-meaning but overly literal genie

    • It has the power to grant almost any wish
    • But we must phrase our wishes carefully!
    • We will always get what we ask for…
    • …but not always what we wanted.

  • Mastering the R language means learning…

    • How to properly phrase commands
    • How to decipher error messages
    • How to view code from R’s perspective
    • How to detect and correct small mistakes

Introduction to R

Console Live Coding

# Addition and Subtraction

10+3

10-3

10 + 3 # spaces are optional but recommended

10 - 3

# ==============================================================================

# Multiplication and Division

10 * 3 # correct

10 x 3 # error

10 / 3 # correct

10 \ 3 # error

# ==============================================================================

# Parentheses and Exponents

10 + 3 * 2

(10 + 3) * 2

10 ^ 2

10 ^ (1 / 2)

# ==============================================================================

# Negative and Large Numbers

10 + -30

9876543 # correct

9,876,543 # error

9 876 543 # error

Introduction to R

Assignment

  • It is often useful to store data in named objects
    • This makes the data easier to use and re-use
    • This makes the code easier to write and read
  • Which command is easier to follow?
    1. Dial 7 8 5 8 6 4 0 8 4 1
    2. Call Office Phone
  • Named objects are created using assignment
    • Give a name then an arrow then the data

office <- 7858640841

Introduction to R

Assignment Live Coding

# LESSON: Assigning and printing

x <- 2
x

# ==============================================================================

# USECASE: Using an object in math (a la algebra) 

x * 4

2 * 4

# ==============================================================================

# LESSON: You must use assignment to update an object

x

x + 1

x # still 2

x <- x + 1
x # updated to 3

# ==============================================================================

# USECASE: We can use the same object multiple times in a line

(10 + x - 1) / x

# ==============================================================================

# USECASE: We can also use an object to create another object

y <- 10 + x
y

# ==============================================================================

# USECASE: We can also use multiple objects in a line

y / x

Introduction to R

Naming

  • Object names can only include:
    • Letters: a-Z
    • Numbers: 0-9
    • Underscores: _
    • Periods: .
  • Additional Rules:
    • Must start with a letter or period
    • Cannot contain spaces or dashes
    • Cannot contain other symbols
    • Names are case-sensitive (age ≠ Age)

Introduction to R

Naming Live Coding

# LESSON: Good names are a balancing act

x <- 93 # what is it?

rate <- 93 # too short

heart_rate_in_beats_per_minute <- 93 # too long

heart_rate_bpm <- 93 # just right

# ==============================================================================

# PITFALL: Don't try to include spaces or dashes in names

heart rate <- 93 # error

heart-rate <- 93 # error

# ==============================================================================

# PITFALL: Don't try to include special symbols

age@time2 <- 12 # error

age_time2 <- 12 # correct

# ==============================================================================

# PITFALL: Don't try to put a number or underscore first

heart_rate_1 <- 93 # correct

1_heart_rate <- 93 # error

_heart_rate <- 93 # error

# ==============================================================================

# LESSON: Object names are case-sensitive

heart_rate <- 93

Heart_rate <- 88

heart_rate # still 93

Heart_rate # a new object

Introduction to R

Functions

  • Recipes allow chefs to cook up tasty treats
    • Recipes call for ingredients
    • Recipes involve one or more steps
    • Steps transform ingredients into treats
  • Functions are like customizable recipes
    • Functions call for inputs (“arguments”)
    • Functions involve one or more lines of code
    • Code transforms inputs into outputs
    • Using functions requires parentheses (usually)

out <- f(in1, in2)

Introduction to R

Functions Live Coding

# USECASE: Function can perform a task more easily and readably

# TEMPLATE: output <- function_name(input)

9 ^ (1 / 2)

x <- sqrt(9)
x

# ==============================================================================

# LESSON: We can also use functions to transform objects

y <- 9

sqrt(y)

# ==============================================================================

# LESSON: We can even use functions to transform the result of calculations

2 / 3

round(2 / 3)

# ==============================================================================

# LESSON: We can customize what a function does using arguments

# TEMPLATE: output <- function_name(argument, argument_name = argument_value)

round(2 / 3, digits = 2)

round(2 / 3, digits = 3)

# ==============================================================================

# LESSON: Some arguments are optional because they have default values

round(2 / 3) # the default value for digits is 0

round(2 / 3, digits = 0)

Introduction to R

Strings

  • When talking to R, we need a way to distinguish
    • Object/function names (e.g., the mean function)
    • Text/character data (e.g., the word mean)
  • Strings are R’s way of storing text data
    • Strings can store any characters (no rules!)
    • Strings are created and displayed with quotes
  • R has great tools for working with strings
    • Strings can be collected into vectors
    • Special functions can transform strings

name <- "John Doe"

Introduction to R

Strings Live Coding

# USECASE: Strings are the main way to store character data in R
 
my_color <- red # error

my_color <- "red" # correct

# ==============================================================================

# USECASE: Strings can also store symbols not allowed in object names

dye <- "red#40"
dye

dyes <- c("red#40", "blue#02")
dyes

# ==============================================================================

# PITFALL: Many operations you can do to numbers won't work for strings

dyes + 1 # error

mean(dyes) # error

# ==============================================================================

# USECASE: But other operations work for both or even just for strings

length(dyes)

nchar(dyes)

dyes2 <- toupper(dyes)
dyes2

Introduction to R

Packages

  • Cookbooks are a great way to learn to cook
    • They contain lots of recipes and instructions
    • Browse an online bookstore for a cookbook
    • Order it to add it to your personal bookshelf
    • To use, pull the cookbook off the shelf
  • Packages are like cookbooks for R
    • They contain helpful functions and datasets
    • Browse an online repository for a package
    • Install it to add it to your personal library
    • To use, load the package from the library

library("pkg_name")

Introduction to R

Packages Live Coding

# USECASE: The stringr package adds a function to fix capitalization

students <- c("mary anne", "BENjamin", "Lee")

# ==============================================================================

# PITFALL: But we can't use that function without installing the package

str_to_title(students) # error

# ==============================================================================

# LESSON: Installing a package using RStudio

# - RStudio > Extras pane > Packages tab > Install button

# ==============================================================================

# PITFALL: We also need to load the package before we can use it

str_to_title(students) # error

# ==============================================================================

# LESSON: We load the package using library()

library("stringr")
str_to_title(students) #finally works!

# ==============================================================================

# LESSON: We can also keep our packages updated using RStudio

# RStudio > Extras pane > Packages tab > Update button

Introduction to R

Importing and Exporting

  • Data is usually stored in data files
    • Importing files into R is called reading
    • Exporting files from R is called writing
  • A convenient data file type is a CSV
    • This stands for comma-separated values
    • A CSV file is easy to share with other people
  • The tidyverse package can read/write CSVs
    • Other packages can read/write other types (e.g., readxl, haven, rio, googlesheets4)

Introduction to R

Read/Write Live Coding

# SETUP: Load the tidyverse package (if you haven't yet)

library(tidyverse)

# ==============================================================================

# USECASE: Read in a file containing data

my_data <- read_csv("jz2017.csv")
my_data

# NOTE: read_csv() will examine and guess the data type of each variable.
# You can tell it the data type of each variable, but that is more advanced.

# ==============================================================================

# USECASE: Write data back to a file

write_csv(my_data, file = "jz2017b.csv")

# NOTE: You can see the new file in Extras pane > Files tab.
# You can open the file in another program (e.g., Microsoft Excel).
# You can also email this file to someone else to share it.

Introduction to R

Introduction to {circumplex}